Multi S-graphs: A Collaborative Semantic SLAM architecture

Add code
May 05, 2023
Miguel Fernandez-Cortizas, Hriday Bavle, Jose Luis Sanchez-Lopez, Pascual Campoy, Holger Voos

Figure 1 for Multi S-graphs: A Collaborative Semantic SLAM architecture
Figure 2 for Multi S-graphs: A Collaborative Semantic SLAM architecture
Figure 3 for Multi S-graphs: A Collaborative Semantic SLAM architecture
Figure 4 for Multi S-graphs: A Collaborative Semantic SLAM architecture

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon

Collaborative Simultaneous Localization and Mapping (CSLAM) is a critical capability for enabling multiple robots to operate in complex environments. Most CSLAM techniques rely on the transmission of low-level features for visual and LiDAR-based approaches, which are used for pose graph optimization. However, these low-level features can lead to incorrect loop closures, negatively impacting map generation.Recent approaches have proposed the use of high-level semantic information in the form of Hierarchical Semantic Graphs to improve the loop closure procedures and overall precision of SLAM algorithms. In this work, we present Multi S-Graphs, an S-graphs [1] based distributed CSLAM algorithm that utilizes high-level semantic information for cooperative map generation while minimizing the amount of information exchanged between robots. Experimental results demonstrate the promising performance of the proposed algorithm in map generation tasks.

* Presented as a candidate to the Distributed Graph Algorithms for Robotics Workshop at ICRA23  
View paper onarxiv icon

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon